In order to address this problem, we need an efficient method to generate acoustic data adapted to the environmental changes that can complement the lack of data in multiple environments. Traditionally, the machine learning community has devised various methods to face the overfitting issue, such as regularization [1]}, transfer learning[2]}, [3]}, and data augmentation [4]}. Transfer learning has been acknowledged to be an effective method to expand the amount of training data by reusing a pre-trained model
and transferring knowledge learned from one environment to another as a starting point [5]}, [6]}. Data augmentation is a suite of techniques that enhance the size and quality of training datasets such that better deep learning models can be built using them [4]}.
